Analyze Users & Optimize Product Retention

本课程是多个项目的一部分。

位教师：Hurix Digital

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2个模块

深入了解一个主题并学习基础知识。

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2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Clustering-based user segmentation uncovers behavior patterns for better personalization and targeting.
Retention methods shape insights—choosing the right one ensures accurate product health assessment.
Identifying power users enables better retention, feature design, and lifetime value growth.
Clear communication and documentation turn technical analysis into actionable, team-wide impact.

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作业

5 任务¹

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授课语言：英语（English）

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该课程共有2个模块

Transform your product analytics capability with advanced user segmentation and retention optimization techniques. This course empowers data analysts to move beyond surface-level metrics to uncover deep behavioral patterns that drive product success.

By completing this course, you'll master the application of k-means clustering to identify distinct user segments and gain the analytical sophistication to evaluate rolling-cohort versus N-day retention methods for strategic decision-making. You'll learn to profile power users through RFM analysis, create compelling data narratives for stakeholders, and publish technical guidance that elevates your team's analytical capabilities. This course is unique because it bridges the gap between technical implementation and business impact, teaching you to transform raw user data into actionable product insights that directly influence retention and growth strategies. To be successful in this course, you should have experience with data analytics, basic understanding of machine learning concepts, and familiarity with Python or similar analytical tools.

Learners will master k-means clustering implementation using scikit-learn to segment users based on RFM variables, enabling them to create data-driven user profiles that inform product strategy and targeted interventions.

涵盖的内容

1个视频2篇阅读材料2个作业

1个视频总计4分钟

Why Customer Segmentation Drives Product Success 4分钟

2篇阅读材料总计20分钟

K-Means Clustering Fundamentals for Customer Analytics 10分钟
RFM Analysis Framework: Strategic Customer Segmentation for Product Analytics 10分钟

2个作业总计33分钟

Build Customer Segments Using K-Means Clustering 18分钟
User Clustering and RFM Analysis Knowledge Check 15分钟

Learners will analyze different retention calculation methodologies, understand their strategic implications, and create technical recommendations that guide data-driven retention strategy decisions in product analytics contexts.